Risk Management & Predictive Analytics

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CRIF's predictive analytics tools and software generate hundreds of millions of score calculations and risk decisions every year around the world.

Predictive Analytics for Risk Management

Predictive analytics is the practice of extracting information from existing data in order to determine patterns and predict future outcomes and trends. It forecasts what might happen in the future with an acceptable level of reliability, and includes what-if scenarios and risk assessment.

Recognized by Gartner, CRIF's expertise in predictive analytics, is demonstrated by the development of numerous scoring projects in many including Bureau scoring models, spanning over 18 countries which in total are used to make hundreds of millions of score calculations and decisions every year around the world.

Rating systems are a core competency in CRIF, thanks to CRIF's Rating Agency experience, we provide rating model development from estimation, validation and review to calibration and evaluation of economic groups.

Credit Risk Score development

Data management to extract value from data businesses need solutions to help them extract, align and distil what’s important and quickly determine analytical interpretations.

Scoring models that allow optimisation of any financial institution process , which they are developed for. CRIF provides a full portfolio of modelling tools and expertise, empowering business analysts, from beginners to advanced modellers, to develop, build, test, deploy and manage predictive models. Types of scorecards we provide are:

Marketing scoring models serve as a real aid in boosting the profitability of an existing portfolio and attracting the most lucrative prospects;

Fraud scoring models lead to optimizing the fraud risk control concentrating the verification on a reduced number of cases both for supporting securitization with pool audit services and IFRS 9 evaluations;

Risk Management advanced model

Internal Rating System (Basel Compliant) Advanced Rating systems (Both Corporate and Retail) model development from estimation, validation and review to calibration and evaluation of economic groups including Probability of Default (PD), Loss Given Default (LGD) and Exposure at Default (EAD) models and encompassing both quantitative and judgmental approaches;

Financial models including credit sustainability indexes which determine the capacity of the customer to take on debt and incorporate Financial Stress Index, Credit Limit & Household budget.

Pricing risk based model, such as Risk Based Pricing in order to calibrate each to the risk profile of the customer and optimise the cost structure of the institution;

Fair value, for assessing the life time value of a retail portfolio, leveraging CRIF data.

Predictive analytics is a key component and integrated part of many of our offerings including our credit management platform products (like StrategyOne) and services, with many success stories to demonstrate how this component can help saving costs and having a faster response, and as well as to allow consistent evaluation treatment.

A clear example of how predictive analytics can improve the credit process is Sprint, CRIF’s Cloud-based Origination-Solution-as-a-Service, which over 300 institutions use every day for credit risk evaluation, credit origination and debt collection. Sprint has 70 scoring models included, which have been developed by CRIF for the assessment of personal loans, retail finance, mortgages, overdrafts, credit cards and business credit.